SEARCH · Engineering Papers
Results for “Machine learning prediction”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Machine learning of ultrasonic data for expansion prediction of concrete with alkali-silica reaction
Explore the source record for details and available documents.
Accelerating phase-field based predictions via surrogate models trained by machine learning methods.
Abstract not provided.
A Machine Learning-Based Analysis for Efficient Predictive Modeling of Negative Hydrogen Ion Sources.
Abstract not provided.
Accelerating Phase-field Predictions Via Surrogate Models Trained By Machine Learning Methods .
Abstract not provided.
A Machine Learning Approach to Part Scale Microstructure Predictions in LPBF
Explore the source record for details and available documents.
Predicting Hydrogen Production from Biomass and Plastic Waste Gasification using Machine Learning and Genetic Algorithms
Machine learning (ML) models were developed to predict process various conditions and compositions by creating a model from a database with approximately 1000 entries and 24 independent variables.
Rapid model prediction and history matching using scientific machine learning for geologic carbon storage at the Illinois Basin-Decatur Project site
Explore the source record for details and available documents.
Machine Learning Application for Smart Network Traffic Prediction
Explore the source record for details and available documents.
Using Machine Learning and Fluid Flow Modeling to Predict the Response of Marine Sediments around the Cascadia Margin to Gas Formation and Hydrate Dissociation
Explore the source record for details and available documents.
Accelerating phase-field based predictions via surrogate models trained by machine learning methods.
Abstract not provided.
Accelerating phase-field based predictions via surrogate models trained by machine learning methods.
Abstract not provided.
Prediction of Material Dielectric Permittivity by Machine Learning
IMPI 55 Virtual Symposium, Virtual, June 28-July 1, 2021
An interpretable machine learning model for advancing terrestrial ecosystem predictions
We apply an interpretable Long Short-Term Memory (iLSTM) network for land-atmosphere carbon flux predictions based on time series observations of seven environmental variables. iLSTM enables interpretability of variable importance and variable-wise temporal importance to the prediction of targets by exploring internal network structures. The application results indicate that iLSTM not only improves prediction performance by capturing different dynamics of individual variables, but also reasonably interprets the different contribution of each variable to the target and its different temporal relevance to the target. This variable and temporal importance interpretation of iLSTM advances terrestrial ecosystem model development as well as our predictive understanding of the system.
A Machine Learning Approach To Fast Statistical Microstructure Predictions In Laser Powder Bed Fusion Additive Manufacturing: Exit Seminar Presentation
Explore the source record for details and available documents.
Digital Rocks Portal (Digital Porous Media): Connecting data, simulation and community
Digital Rocks Portal (DRP, https://www.digitalrocksportal.org) organizes and preserves imaged datasets and experimental measurements of porous materials in subsurface, and beyond, with the mission to connect them to simulation and analysis, as well as educate the research community. We have over 150 projects represented in more than 200 publications, and an active community that reuses the data, most recently in multiple machine learning applications for automating image analysis as well as the prediction of transport. Such automation is crucial for performing formation evaluation tasks in near-real time. We present benchmark datasets that have played a role in recent machine learning prediction successes in the field. We further discuss the vision for further research advances, educational materials, as well as growth and sustainability plan of this digital rock physics community resource. In particular, we are in the process of expanding into a broader repository of engineered porous materials, specifically those for energy storage and the portal will transition to Digital Porous Media (DPM) in near future.
Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance
Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. Here, we finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.
Quantitative Prediction of Perovskite Stability Using Accelerated Testing and Machine Learning
PV technologies based on hybrid perovskites offer the potential for reducing solar cell costs, but they are particularly vulnerable to degradation by environmental factors such as moisture, oxygen, and illumination. Commercialization will require not only stable materials and device architectures but also accelerated testing protocols and models that can predict degradation from the accelerated testing data. Here, we report results from in situ photoluminescence (PL) and photoconductivity (PC) measurements during perovskite degradation with simultaneous optical transmittance (Tr) measurements or reflected dark field (DF) imaging. From PL, PC, Tr, and DF, we determine (respectively) the steady-state quasi-Fermi level splitting, the mean effective carrier diffusion length, the extent of conversion of perovskite to higher bandgap degradation products, and the extent of scattering from domains with different orientation or composition, all as a function of time during degradation. Simultaneous measurement of PL-PC-Tr or PL-PC-DF on perovskite absorbers in an environmental chamber over a wide range of humidity, oxygen, temperature, and illumination levels yields a rich data set. We use machine learning to develop a model that accurately predicts (within 10%) the time for the diffusion length to decrease to 85% of its initial value. The model takes the environmental conditions and the first few measurements from the PL-PC-Tr or PL-PC-DF experiment as input. Thus, the model provides a framework to interpret the results of accelerated testing of absorber materials. One of the dominant features in the model of degradation for CH 3 NH 3 PbI 3 is the initial rate at which transmittance increase. For devices with opaque contacts, the pixel-averaged rate of change of the intensity in dark-field images can be used in place of transmittance. Results are also presented on full PV devices with in-situ current-voltage (JV) measurements. Here, the data from simultaneous PL-DF-JV under environmental stresses reveal intimate connection between degradation and shunts and provide a framework to extend accelerated testing to devices. Further, the presentation reveals underlying universal behavior in degradation pathways over a broad range of environmental stresses and perovskite compositions.